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Record W2907546350 · doi:10.1039/c8lc01256c

Two-level submicron high porosity membranes (2LHPM) for the capture and release of white blood cells (WBCs)

2019· article· en· W2907546350 on OpenAlexafffund
Javier Alejandro Hernández-Castro, Kebin Li, Jamal Daoud, David Juncker, Teodor Veres

Bibliographic record

VenueLab on a Chip · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcGill University and Génome Québec Innovation CentreMcGill UniversityNational Research Council Canada
FundersNational Research Council CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaMcGill University
KeywordsPorosityMembraneChemical engineeringMaterials scienceChemistryBiomedical engineeringChromatographyBiophysicsComposite materialEngineeringBiologyBiochemistry

Abstract

fetched live from OpenAlex

A method modifying a vacuum-assisted UV micro-molding (VAUM) process is proposed for the fabrication of polymer two-level submicron high porosity membranes (2LHPM). The modified process allows for the fabrication of robust, large-area membranes over 5 × 5 cm2 with a hierarchical architecture made from a 200 nm-thick layer having submicron level pores (as small as 500 nm) supported by a 20 μm-thick layer forming a microporous structure with 10-15 μm diameter pores. The fabricated freestanding membranes are flexible and mechanically robust enough for post manipulation and filtration of cell samples. Very high white blood cell (WBC) capture efficiencies (≈97%) from healthy blood samples after red blood cell (RBC) lysis are demonstrated using a 3D-printed filter cartridge incorporated within these 2LHPM. A high release efficiency of ≈95% is also proved using the same setup. Finally, on-filter multistep immunostaining of captured cells is also shown.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.184
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2019
Admission routes2
Has abstractyes

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